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Joint image compression and encryption using a novel column-wise scanning and optimization algorithm

Procedia Computer Science 2020年 167卷 244-253页

作者： Pratibha Chaudhary Ritu Gupta Abhilasha Singh Pramathesh Majumder Ayushi Pandey

In this research, a joint image compression and encryption scheme is presented which works for different image dimensions 256X256, 512X512 and 1024X1024 and different image sizes. The proposed scheme is efficient as compression and encryption is done simultaneously which makes computations faster and secure at the same time. The compression scheme deployed is like JPEG compression but uses column-wise scanning and optimization instead of zigzag scanning. In entropy phase coding, Huffman coding of JPEG compression is replaced with Arithmetic coding. Image encrypted using XOR encryption is unbreakable, as used earlier in one-time pad. Compressed data is XORed with randomly generated number. The proposed scheme is implemented for grayscale images using MATLAB software version 2016a. The experimental results showed that the scheme provides good compression ratio, secures against attacks and takes less computation time. This can be further extended for RGB images of different dimension 256X256, 512X512 and 1024X1024 and different image sizes.

关键词： Column-wise scanning optimization algorithm DCT (Discrete cosine transform) Joint Compression Encryption JPEG Compression Quantization XOR Encryption

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Root Based optimization algorithm for Task-Oriented Structural Design of a Multi-Axial Road Test Rig

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IEEE ACCESS 2020年 8卷 168061-168078页

作者： Wang, Xiao Cong, Dacheng Yang, Zhidong Han, Junwei Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China

A novel root based optimization algorithm (ROA), which mimics the proliferation of plant roots, is proposed to evaluate the performances of a multi-axial road test rig over a prescribed workspace. Plant roots have developed complex behavior patterns to search for nutrients in the soil and even exhibit swarm intelligence. The growing roots can adapt their actions such as elongation, branching, and tropistic movement according to the environment. During the process of foraging for water and nutrients, the adaptive growth behavior of roots can be simulated to tackle the optimization problem. The efficiency of the proposed ROA has been validated by comparing it with well-known intelligent algorithms using classical and IEEE CEC 2019 benchmark functions. Computational results indicate that ROA outperforms other algorithms to search a global optimum on several benchmarks. Furthermore, the results are tested with nonparametric statistical methods, e.g., Wilcoxon rank-sum test. Besides, ROA is applied to the optimal dimensioning synthesis of the multi-axial road test rig (MRTR) based on the orthogonal 6-RSS parallel manipulator to promote the device's performance. The inverse kinematics of the multi-axial road test rig based on the asymmetrical orthogonal 6-RSS parallel mechanism is analyzed in detail. Applying the achieved dimensionless Jacobian, the performance criteria involving the global isotropy index, the force/velocity transmissibility index, the decoupling index are established. By simultaneously taking account the proposed criteria of performance, structural optimization is carried out resorting to ROA. Finally, a set of optimized dimensional parameters is obtained. The practical application further illustrates that ROA has the potential to deal with complex optimization problems.

关键词： optimization Roads Particle swarm optimization Indexes Benchmark testing Kinematics Space exploration Root adaptive growth optimization algorithm benchmark test orthogonal 6-RSS parallel manipulator task-oriented performance criteria optimal structural design

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A novel two-variable optimization algorithm of TCA for the design of face gear drives

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MECHANISM AND MACHINE THEORY 2022年 175卷

作者： Lu, Xinxin Zhou, Yuansheng He, Dong Zheng, Fangyan Tang, Kai Tang, Jinyuan Cent South Univ State Key Lab High Performance Complex Mfg Changsha 410083 Peoples R China Cent South Univ Coll Mech & Elect Engn Changsha 410083 Peoples R China Hong Kong Univ Sci & Technol Dept Mech & Aerosp Engn Hong Kong Peoples R China Wuhan Univ Technol Sch Automot Engn Hubei Key Lab Adv Technol Automot Parts Wuhan 430070 Peoples R China

Tooth contact analysis (TCA) is a crucial step in evaluating the working performances of gear designs. According to the meshing process, or the equation of meshing, TCA is usually numerically implemented to find contact points on tooth surfaces with a highly nonlinear model, which contains five independent equations and five variables. It is computationally expensive to solve this model, especially for spatial gears with a complicated tooth geometry such as face gears. To solve this problem, we propose a novel method to simplify the optimization model to two variables. Specifically, the rotation relationship between the face gear and pinion is analyzed in a kinematic view to mathematically eliminate three parameters in the meshing equation. The geometric characteristic of face gear drives is also studied to constrain the parameter range at a reasonable computation cost. Furthermore, this optimization model is solved by a two-stage algorithm with high stability and efficiency, realized by the particle swarm optimization (PSO) algorithm and the Quasi-Newton method, respectively. Examples are given to verify the efficiency and stability of the proposed method.

关键词： Tooth contact analysis Face gears Kinematic view Geometric characteristic optimization algorithm

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M-Net: A Skin Cancer Classification With Improved Convolutional Neural Network Based on the Enhanced Gray Wolf optimization algorithm

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024年第6期34卷

作者： Xu, Zhinan Zhang, Xiaoxia Liu, Luzhou Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan Peoples R China

Skin cancer is a common malignant tumor causing tens of thousands of deaths each year, making early detection essential for better treatment outcomes. However, the similar visual characteristics of skin lesions make it challenging to accurately differentiate between lesion types. With advancements in deep learning, researchers have increasingly turned to convolutional neural networks for skin cancer detection and classification. In this article, an improved skin cancer classification model M-Net is proposed, and the enhanced gray wolf optimization algorithm is combined to improve the classification performance. The gray wolf optimization algorithm guides the wolf pack to prey through a multileader structure and gradually converges through the encirclement and pursuit mechanism, so as to perform a more detailed search in the later stage. To further improve the performance of the gray wolf optimization, this study introduces the simulated annealing algorithm to avoid falling into the local optimal state and expands the search range by improving the search mechanism, thus enhancing the global optimization ability of the algorithm. The M-Net model significantly improves the accuracy of classification by extracting features of skin lesions and optimizing parameters with the enhanced gray wolf optimization algorithm. The experimental results based on the ISIC 2018 dataset show that compared with the baseline model, the feature extraction network of the model has achieved a significant improvement in accuracy. The classification performance of M-Net is excellent in multiple indicators, with accuracy, precision, recall, and F1 score reaching 0.891, 0.857, 0.895, and 0.872, respectively. In addition, the modular design of M-Net enables it to flexibly adjust feature extraction and classification modules to adapt to different classification tasks, showing great scalability and applicability. In general, the model proposed in this article performs well in the classification of

关键词： deep learning optimization algorithm skin cancer skin lesion classification

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Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks optimization algorithm

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IEEE ACCESS 2020年 8卷 133076-133085页

作者： Golilarz, Noorbakhsh Amiri Mirmozaffari, Mirpouya Gashteroodkhani, Tayyebeh Asgari Ali, Liaqat Dolatsara, Hamidreza Ahady Boskabadi, Azam Yazdi, Mohammad Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX 76019 USA Univ Guilan Dept Elect Engn Rasht *** Iran Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Clark Univ Sch Management Worcester MA 01610 USA Washington State Univ Carson Coll Business Dept Finance & Management Sci Pullman WA 99163 USA Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal

In this research, we propose to utilize the newly introduced Multi-population differential evolution-assisted Harris Hawks optimization algorithm (CMDHHO) in the optimization process for satellite image denoising in the wavelet domain. This optimization algorithm is the improved version of the previous HHO algorithm which consists of chaos, multi-population, and differential evolution strategies. In this study, we applied several optimization algorithms in the optimization procedure and we compared the de-noising results with CMDHHO based noise suppression as well as with the Thresholding Neural Network (TNN) approaches. It is observed that applying the CMDHHO algorithm provides us with better qualitative and quantitative results comparing with other optimized and TNN based noise removal techniques. In addition to the quality and quantity improvement, this method is computationally efficient and improves the processing time. Based on the experimental analysis, optimized based noise suppression performs better than TNN based image de-noising. Peak Signal to Noise Ratio (PSNR) and Mean Structural Similarity Index (MSSIM) are used to evaluate and measure the performance of different de-noising methods. Experimental results indicate the superiority of the proposed CMDHHO based satellite image de-noising over other available approaches in the literature.

关键词： optimization Image denoising Wavelet transforms Signal processing algorithms Noise reduction Wavelet domain Thresholding (Imaging) CMDHHO optimization algorithm satellite image de-noising TNN wavelet domain

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Severity Assessment of Cotton Canopy Verticillium Wilt by Machine Learning Based on Feature Selection and optimization algorithm Using UAV Hyperspectral Data

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REMOTE SENSING 2024年第24期16卷 4637-4637页

作者： Li, Weinan Guo, Yang Yang, Weiguang Huang, Longyu Zhang, Jianhua Peng, Jun Lan, Yubin South China Agr Univ Coll Elect Engn Coll Artificial Intelligence Guangzhou 510642 Peoples R China South China Agr Univ Natl Ctr Int Collaborat Precis Agr Aviat Pesticide Guangzhou 510642 Peoples R China Chinese Acad Agr Sci Nanfan Res Inst Sanya 572024 Peoples R China Chinese Acad Agr Sci Cotton Res Inst Anyang 455000 Peoples R China Chinese Acad Agr Sci Agr Informat Inst Beijing 100081 Peoples R China

Verticillium wilt (VW) represents the most formidable challenge in cotton cultivation, critically impairing both fiber yield and quality. Conventional resistance assessment techniques, which are largely reliant on subjective manual evaluation, fail to meet the demands for precision and scalability required for advanced genetic research. This study introduces a robust evaluation framework utilizing feature selection and optimization algorithms to enhance the accuracy and efficiency of the severity assessment of cotton VW. We conducted comprehensive time-series UAV hyperspectral imaging (400 to 995 nm) on the cotton canopy in a field environment on different days after sowing (DAS). After preprocessing the hyperspectral data to extract wavelet coefficients and vegetation indices, various feature selection methods were implemented to select sensitive spectral features for cotton VW. By leveraging these selected features, we developed machine learning models to assess the severity of cotton VW at the canopy scale. Model validation revealed that the performance of the assessment models responded dynamically as VW progressed and achieved the highest R2 of 0.5807 at DAS 80, with an RMSE of 6.0887. optimization algorithms made a marked improvement for SVM in severity assessment using all observation data, with R2 increasing from 0.6986 to 0.9007. This study demonstrates the potential of feature selection and machine learning methods based on hyperspectral data in enhancing VW management, promising advancements in high-throughput automated disease assessment, and supporting sustainable agricultural practices.

关键词： cotton Verticillium wilt unmanned aerial vehicle hyperspectral imaging feature selection optimization algorithm disease severity

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Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction

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APPLIED ENERGY 2021年 292卷 116883-116883页

作者： Roslan, M. F. Hannan, M. A. Ker, Pin Jern Begum, R. A. Mahlia, T. M. Indra Dong, Z. Y. Univ Tenaga Nas Dept Elect Power Engn COE Kajang 43000 Malaysia Univ Kebangsaan Malaysia Inst Climate Change Bangi 43600 Malaysia Univ Technol Sydney Sch Informat Syst & Modelling Sydney NSW Australia Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia

This study deals with the development of an optimal power scheduling controller for energy management of distributed energy resources in the microgrid system. The developed optimized controller is implemented using lightning search algorithm to overcome the uncertainties of microgrid energy management and to provide an optimum power delivery to loads with minimum cost. The primary objectives of the proposed optimized controller are to: (i) develop an optimized controller for microgrids energy management, (ii) minimize the total operating cost of the distributed energy resources units, (iii) reduce the environmental emission, and (iv) solve the complicated constraint optimization problems. The proposed optimization algorithm is implemented in the modified IEEE 14-bus test system to optimize the microgrid power management schedule. The optimized controller is executed based on the real load varying conditions recorded in Perlis, Malaysia. It is observed that the optimized controller successfully reduced the amount of power consumption from 971.65 MW to 364.3 MW which in turn saving cost of RM 265432.06. The proposed scheduling optimized controller performance is compared with the recent reported work of backtracking search algorithm optimization for validation. Result shows that the lightning search algorithm based MG controller produced a cost-effective system with 62.5% of cost saving and 61.98% of carbon dioxide emission reduction which is much higher compared to with MG and backtracking search algorithm based MG optimization, respectively. The effectiveness of the proposed approach outperformed other techniques in terms of minimum total operating cost of distributed energy resources and solving complicated constraints in optimization problems.

关键词： Microgrids Scheduling optimization algorithm Energy management system Controller Uncertainties Distributed energy resource

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Wind Technologies for Wake Effect Performance in Windfarm Layout Based on Population-Based optimization algorithm

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ENERGIES 2021年第14期14卷 4125页

作者： Hsieh, Yi-Zeng Lin, Shih-Syun Chang, En-Yu Tiong, Kwong-Kau Tan, Shih-Wei Hor, Chiou-Yi Cheng, Shyi-Chy Tsai, Yu-Shiuan Chen, Chao-Rong Natl Taiwan Ocean Univ Dept Elect Engn Keelung 202301 Taiwan Natl Taiwan Ocean Univ Ctr Excellence Ocean Engn Keelung 202301 Taiwan Natl Taiwan Ocean Univ Inst Food Safety & Risk Management Keelung 202301 Taiwan Natl Taiwan Ocean Univ Dept Comp Sci & Engn Keelung 202301 Taiwan China Steel Corp Green Energy & Syst Integrat R&D Dept Kaohsiung 81233 Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei 10608 Taiwan

The focus of this study is under the auspices of China Steel Corporation, Taiwan, in carrying out the national energy policy of 2025 Non-Nuclear Home. Under this policy, an estimated 600 offshore wind turbines will be installed by 2025. In order to carry out the wind energy project effectively, a preliminary study must be conducted. In this article, we investigated the influence of the wake effect on the efficiency of the turbines' layout in a windfarm. A distributed genetic algorithm is deployed to study the wind turbines' layout in order to alleviate the detrimental wake effect. In the current stage of this research, the historical weather data of weather stations near the site of the 29th windfarm, Taiwan, were collected by Academia Sinica. Our wake effect resilient optimized windfarm showed superior performance over that of the conventional windfarm. Additionally, an operation cost minimization process is also demonstrated and implemented using an ant colony optimization algorithm to optimize the total length of the power-carrying interconnecting cables for the turbines inside the optimized windfarm.

关键词： wake effects optimization algorithm turbines layout wind distribution

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Block Compressed Sensing Observation Matrix optimization algorithm Based on Block Target

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电脑学刊 2021年第6期32卷 218-226页

作者： Shi-Fu Xu Ya-Nan Jiang

In order to eliminate the blocking effect of block compressed sensing algorithm, an optimization algorithm of block compressed sensing observation matrix based on block target is studied. The theory of block compressed sensing is to segment the original image with fixed size to obtain the sub blocks, arrange the texture of each sub block, use the compressed sensing observation matrix to sample each sub block, and optimize the observation matrix of block compressed sensing to eliminate the blocking effect. The block target method uses sparse orthogonal basis to make the processed block target conform to sparsity and orthogonality. The sparse coefficient vector is obtained by the basis inverse transformation, and the reflection coefficient of block target is obtained by using the sparse coefficient vector to optimize the observation matrix of block compressed sensing. The experimental results show that the peak signal-to-noise ratio (PSNR) is higher than 31dB when the algorithm is applied to image reconstruction, and the relative support set error of different sparsity and observation times is low, which can effectively eliminate the blocking effect of block compressed sensing algorithm.

关键词： block target block compressed sensing observation matrix optimization algorithm PSNR

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Determination of Tracking Errors with Respect to the Geometrical Errors based on optimization algorithm

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Energy Procedia 2014年 49卷 2211-2220页

作者： Feihu Sun Zhifeng Wang Minghuan Guo Fengwu Bai Zijian Xu Key Laboratory of Solar Thermal Energy and Photovoltaic System of Chinese Academy of Sciences Institute of Electrical Engineering.No.6 North ertiaoZhongguancun Haidian district of Beijing China100190

Accurate dual-axis sun tracking is the key feature of a heliostat and is critical for the performance of a solar tower power plant. The primary tracking errors with respect to the geometrical errors could be theoretically determined from the measurements of the BCS based on optimization algorithm. Tests are performed on two heliostats in DAHAN solar tower plant and analyses are performed to evaluate the comprehensive effect of the six angular geometrical errors on the heliostat tracking accuracy. The test results show that the altitude-azimuth tracking angle formulas for several fixed geometrical errors work well and have a effectiveness for a given period of time.

关键词： BCS tracking angle geometrical errors optimization algorithm

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